MongoDB and Cassandra makes up top 10 in DB-Engine ranking list

NoSQL Data stores, MongoDB and Cassandra adoption is gaining rapidly and it is obvious from DB Engine ranking site. Recently these two NoSQL data stores made an entry to top 10 database list where as HBase and CouchDB stand at no 14 and 15 respectively.

DB-Engines rank site has updated and added many new databases in to its list. The DB-Engine ranking is one of the interesting use cases for populating data from social networks and use of big data to understand the trend patterns.

DB-Engine uses various popular social sites to acquire and understand data. They collect data from search engines, Google trends, Stack overflow, Indeed job portal and Linkedin. Most importantly, DB-Engine rank does not depend on the technical details or the underlined database architecture and transactions per milliseconds which are typically measured in TPC rating (http://www.tpc.org/tpcc/).

However this re-enforces that, NoSQL databases are getting used in many projects and gaining lot of traction. However the popularity index value has a wide distance between Relational databases compared to NoSQL databases. In case of MySQL the index value 1273 and in case MongoDB it is 101( and order of 10). As the transactional systems installation base is very high for many many years compared to NoSQL data stores. So number of discussions and more number of search results would be resulted. This could be one of the main reason. 

Please find the URL : http://db-engines.com/en/ranking

An alternative framework for Mahout : CRAB

Have you imagined every time you purchase any item from online sites like Amazon, BestBuy etc you might have figured out there are other items have been displayed as recommendations for you. For example I buy book from Amazon, it recommends list of books purchased by other shoppers with similar interest. This is possible as the online stores actual process millions of data and finds out the item purchased by similar users who are having a common buying behaviour. This helps the online sites sell more to their users based on the user preferences and their online behaviours. Most of the times users will not know what items they are looking for over the net. The recommendation system helps them to discover similar items based on their interest.  

In today’s overcrowded world with millions of items, it is very difficult to search and narrow down our requirements. In that context the online stores provides a filtration of data and presented in most pleasant way. At times we discover items which we might not heard of.

The same is not only true for online retailers. In most of the social sites, we discover friends and people with similar interest. This is done by processing all the socio interests expressed over the net and finding similarities between them. In linkedin you will find jobs, professionals, groups with similar interest. This is a facilitated by underline recommendation system infrastructure. Building a recommendation system could become a fairly complex process as the number of variables are going to increase. Of course the important variable is your amount of data to be processed.

Mahout played a very critical role in solving this problem. But it is not that trivial to build applications with Mahout. Though, it provides a comprehensive set of tools to work with Machine Learning. This is where CRAB fits the bill. The main objective of CRAB is to provide a very simple way to build the recommendation engine.

Crab is a flexible, fast recommender engine for python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (NumPy, SciPy, Metaplotlib).

The project is started in 2010 by Muricoca incorporated as an alternative to Mahout. It is developed using python so it is much easier to work with for an average programmer compared to Mahout which is built using Java. It has implemented User based, Item based and sloped based Collaborative filtering algorithms.

Demo Example can be found at:

https://github.com/marcelcaraciolo/crab/blob/master/crab/tests/test_recommender.py

Find more at:  https://github.com/muricoca/crab

Resource : http://www.slideshare.net/marcelcaraciolo/crab-a-python-framework-for-building-recommender-systems

Hadoop simplified frameworks

There are many frameworks available for reducing the complexity involved in writing MapReduce programs for Hadoop.

In this arcticle I have discussed few of them and most of them are actively developed and have many production implementation. These framework will increase your productivity as they provide high level features wrapping the low level complexity. Few of them even allow you write directly java code with out thinking MapReduce.

Before using them make sure you will evaluate and understand the frameworks better so that you will not end up in selecting  wrong framework. Also think of your long term needs. In my experience any framework provide the basic tenets but as you explore and progress, you will have issues in doing writing thing in write way. Its going to be even more worst, in case those features are not actively supported by the framework. It is no different from selecting any other frameworks for any other job. All those rules applied. 

1. Cascading

2. Pangool

3. Pydoop

4. MRJobw 

5. Happy

6. Dumbo

Cascading :

• It is an abstraction layer works on top Apache hadoop.
• Can create and execute complex workflows
• Works with any JVM based language ( Java, Ruby , Clojure )
• Primarily workflow gets executed using pipes.
• It  follows “source-pipe-sink” paradigm, where data is captured from different sources, follows reusable  ‘pipes’ that perform data analysis process.
• The developers can write JVM based code without really thinking MapReduce.
• Supported commercially by Concurrent inc.
• URL : http://www.cascading.org

Pangool :

• Works on most of the Hadoop distribution.
• Easier map reduce development.
• Support for Tuples instead of just key/value pairs.
• Efficient and easy to use secondary sorting
•  Efficient, easy to use reduce-side joins
• Performance and flexibility like Hadoop without really worrying about the Hadoop complexit
• First -class multiple inputs and outputs
• Built in serialization support with thrift and protostuff
• Commercial support  from DataSalt
• URL : http://pangool.net/

Pydoop :

• Provides simple python API for MapReduce.
• It is based CPython package and being a CPython module provides access to an extensible set of python libs like numPY, sciPy etc.
• More interactive High level Hadoop API available for executing complex jobs.  
• Provides high level of HDFS API
• Developed by http://www.crs4.it/
• URL : pydoop.sourceforge.net

MRJob :

• Simplified MapReduce scripts.
• Developed by Yelp and actively being used in many production environments.
• Built for Hadoop. Built using python.
• Simple to use compared direct python streaming.
• Available for running Hadoop on Amazon Elastic map reduce (EMR).
• Can be used for running complex Machine Learning algorithms and log processing on Hadoop cluster.
• URL: https://github.com/Yelp/mrjob

Happy :

• Simplified Hadoop framework built on Jython .
• Map-reduce jobs in Happy are defined by sub-classing happy.HappyJob and implementing a map(records, task) and reduce(key, values, task) function.
• Using run() you can execute the job.
• It also can be used for complex data processing and implemented in production environment.
• URL : http://code.google.com/p/happy/

Dumbo :

• Dumbo is also built using python.
• Python API for writing MapReduce.
• All the low level features nicely wrapped and provided as unix pipes.
• It has many nice inbuilt functions/APIs to write highlevel MapReduce scripts.
• URL : https://github.com/klbostee/dumbo

Map Reduce code in python for getting highest score

My Data set : ( scores.dat )

sachin,1996,86

sachin,1996,75

sachin,1996,145

sachin,1996,98

sachin,1997,97

sachin,1997,65

sachin,1996,98

sachin,1996,54

sachin,1998,98

sachin,1998,53

sachin,1997,34

sachin,1997,54

sachin,1997,54

sachin,1997,23

nikhil,1996,56

nikhil,1996,54

nikhil,1997,43

nikhil,1998,89

nikhil,1996,32

nikhil,1997,54

nikhil,1998,45

nikhil,1996,32

nikhil,1996,43

akash,1996,122

akash,1996,98

akash,1997,12

akash,1998,23

akash,1996,87

akash,1997,65

akash,1998,65

akash,1996,32

akash,1996,73

mapper.py ( /user/hduser/mapper.py )


#!/usr/bin/env python

import sys

for line in sys.stdin:
   (val1,val2,val3) = line.strip().split(",")
   print "%s\t%s" % (val1, val3)

reducer.py ( /user/hduser/reducer.py )


#!/usr/bin/env python

from operator import itemgetter
import sys

( last_name , max_val ) = ( None , -sys.maxint )

# input comes from STDIN
for line in sys.stdin:
    # remove leading and trailing whitespace
    name,val = line.strip().split("\t")

    if last_name and last_name != name:
print "%s\t%s" %(last_name , max_val)
(last_name , max_val) = (name, int(val))
    else:
(last_name , max_val) = (name , max(max_val,int(val)))

if last_name:
    print "%s\t%s" % (last_name,max_val)

Test first with in unix shell:
cat scores.dat | python mapper.py | sort -k1,1 | python reducer.py

akash    122
nikhil     89
sachin    145

Map reduce  command for running the job :
${HADOOP_HOME}/bin/hadoop jar ${HADOOP_HOME}/contrib/streaming/hadoop-*streaming*.jar -file /home/hduser/mapper.py -mapper /home/hduser/mapper.py -file /home/hduser/reducer.py -reducer /home/hduser/reducer.py -input /user/hduser/scores -output /user/hduser/scores-out

Attending pycon India 2012

PyCon India 2012:

It is indeed a great nice experience in participating at PyCon India 2012 and presenting Talk on Bigdata, Hadoop and Python.

http://in.pycon.org/2012/funnel/pyconindia2012/42-python-in-big-data-world

I  am sharing my presentation 

Python in big data world from Venkat Sp

Please keep watching this blog. I will also provide all the codes presented during the talk.

Private cloud is not according to Gartner

• Private Cloud Is Not Virtualization

• Private Cloud Is Not Just About Cost Reduction

• Private Cloud Is Not Necessarily On-Premises

• Private Cloud Is Not Only Infrastructure as a Service (IaaS)

• Private Cloud Is Not Always Going to Be Private

Please visit this url ...

http://www.gartner.com/it/page.jsp?id=2157015&goback=%2Egde_61513_member_163225161

Will unifying the user interface for all the personal devices such as personal systems and mobile devices makes sense?

Here is my take on this:

In today’s world there are many personal computing devices. The good old PC and notebooks, netbooks, tablets,  smart phones. Though at a high level they seem to solve more or less same problem but each one has different use cases for their use. All of them complement together but one cannot simply replace the other. May be there could be convergence of these devices and can some of these devices obsolete in future. That is all together a different case for argument.  

As said earlier each device is used for different purpose and for different scenarios. In that case their user interface should such that closely satisfies the target objective and very intuitive for that scenario.  So in this context can we build common unified user interface for all the devices? It improves productivity and reduces the learning curve as the user interface seems to be same on all the devices.

But I would vote against of this as this will hurt innovation. This will really make us stop thinking the customization really required for individual device for what it has been built. May be this is the fundamental reason why Apple departed from its desktop OS and built a complete new OS for mobile devices such as smart phone and mobile phone and people embraced that.  

On the contrary Microsoft has taken a unified approach for building a common interface for all its devices. If one has to think from Microsoft’s view point this strategy may be a right fit, as they have huge legacy of desk top OS and well established community. They definitely want the PC customers migrate to upcoming Windows8. May be that is the pull for Windows 8 on personal systems to have the same interface as like as mobile devices. This will also help them to build a single strong product and make it work on any devices. This is not anything new that Microsoft is trying. Earlier they have tried the same. They built the windows desktop interface on Windows CE and it was not well received by the users.

It is not simply, building the OS that matters, the OS future is dependent on the active community it can create. As Microsoft is late entrant into mobile OS though it has built earlier but failed to make a mark, it wants to capitalize on the existing user community to build applications. That may be a justification to be made but I personal think it will be good to have multiple user interface strategy for different categories of devices as each device has their own consumer strategy.